The blog includes details, source code and examples of our statistical models for DNA-based survey data (qCPR and metabarcoding).

Please see under each section for corresponding material by clicking on the corresponding tab.

List of relevant publications:

Diana, A., Matechou, E., Griffin, J., Yu, D., Luo, M., Tosa, M., Bush, A. and Griffiths, R., 2022. eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring. arXiv preprint arXiv:2211.12213.

Buxton, A., Diana, A., Matechou, E., Griffin, J. and Griffiths, R.A., 2022. Reliability of environmental DNA surveys to detect pond occupancy by newts at a national scale. Scientific reports, 12(1), pp.1-10.

Diana, A., Matechou, E., Griffin, J.E., Buxton, A.S. and Griffiths, R.A., 2021. An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error. Ecography, 44(12), pp.1838-1844.

Buxton, A., Matechou, E., Griffin, J., Diana, A. and Griffiths, R.A., 2021. Optimising sampling and analysis protocols in environmental DNA studies. Scientific Reports, 11(1), pp.1-10.

Griffin, J.E., Matechou, E., Buxton, A.S., Bormpoudakis, D. and Griffiths, R.A., 2020. Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(2), pp.377-392.